National Cohort Study of Long-Term Exposure to PM2.5 Components and Mortality in Medicare American Older Adults

There is increasing evidence linking long-term fine particulate matter (PM2.5) exposure to negative health effects. However, the relative influence of each component of PM2.5 on health risk is poorly understood. In a cohort study in the contiguous United States between 2000 and 2017, we examined the effect of long-term exposure to PM2.5 main components and all-cause mortality in older adults who had to be at least 65 years old and enrolled in Medicare. We estimated the yearly mean concentrations of six key PM2.5 compounds, including black carbon (BC), organic matter (OM), soil dust (DUST), nitrate (NO3–), sulfate (SO42–), and ammonium (NH4+), using two independently sourced well-validated prediction models. We applied Cox proportional hazard models to evaluate the hazard ratios for mortality and penalized splines for assessing potential nonlinear concentration–response associations. Results suggested that increased exposure to PM2.5 mass and its six main constituents were significantly linked to elevated all-cause mortality. All components showed linear concentration–response relationships in the low exposure concentration ranges. Our research indicates that long-term exposure to PM2.5 mass and its essential compounds are strongly connected to increased mortality risk. Reductions of fossil fuel burning may yield significant air quality and public health benefit.


■ INTRODUCTION
Air pollution is among the serious environmental threats to public health. It has been well documented that long-term exposure to fine particulate matter (particles or droplets in the air that are 2.5 μm or less in diameter [PM 2.5 ]) is related to higher mortality and morbidity. 1−4 However, prior research has mainly targeted on the health consequences of PM 2.5 mass concentrations, and the evidence of component-specific effects remains scarce. 5,6 The main compositions of PM 2.5 are complex, and a better awareness of compound-specific health impacts of PM 2.5 could help guide pollution control policies by targeting more particular sources or compounds.
Toxicological and epidemiological studies suggest certain components in PM 2.5 could have a major impact on the documented adverse health effects on humans. Animal experiments show that black carbon (BC) and sulfate (SO 4 2− ) could harm the cardiovascular system acutely and chronically. 7,8 Organic PM 2.5 compounds including elemental carbon (EC) and organic carbon (OC) may have negative effects on the respiratory and immune systems. 9 In addition, recent studies suggest that the synergistic effect of ammonium sulfate and the existence of ultrafine particles could increase the accumulation of peptides that influence the development of neurodegenerative diseases. 10 Moreover, PM 2.5 components of transition metals have been linked to adverse health impacts. Ostro et al. reported copper to be linked to increased ischemic heart disease mortality. 6 Bell et al. suggested a statistically significant relationship between short-term variations in vanadium and nickel concentrations and a higher risk of cardiovascular and respiratory hospitalization. 11 According to prior epidemiological research, short-term exposure to PM 2.5 compounds (e.g., EC, OC, and SO 4 2− ) may be associated with cardiovascular and respiratory outcomes. 12−14 However, the long-term effects of exposure to PM 2.5 compounds are still unclear.
Due to a lack of valid measurements of speciated chemical composition, it has been difficult to assess the health impacts of PM 2.5 components. This question cannot be addressed using monitor measurements alone; particularly, high-resolution PM 2.5 component estimations over a long period of time are required, necessitating modeling with restraints on groundbased measurement.
To overcome these gaps in knowledge, we performed a nationwide population-based cohort study (2000−2017) to examine the relationships between long-term PM 2.5 key component exposure and all-cause mortality among the U.S. Medicare population, using two independently sourced, speciated air pollution data sets. Our study aims to find the main PM 2.5 constituents causing the increase in mortality so that legislation can be developed to control the air quality at specific sources. ■ MATERIALS AND METHODS Study Population. We utilized the Medicare denominator file from the Centers for Medicare and Medicaid Services (CMS), a publicly available privacy-protected national database. Demographic information on sex, age, race, residential ZIP code, Medicaid eligibility (a low socioeconomic status indicator), and date of death were obtained from the denominator file for each Medicare beneficiary annually, and each beneficiary was assigned a unique ID to enable tracking over time. We created an open cohort containing all Medicare beneficiaries who were 65 years or older and residing in the contiguous United States between 2000 and 2017, and allcause mortality was considered the outcome of interest. The CMS and Emory's Institutional Review Board (IRB) have both given their approval for this study (#STUDY00000316 and #RSCH-2020-55733, respectively). The Medicare data set was managed and processed in a secure high-performance computing (HPC) cluster environment, compliant with the Health Insurance Portability and Accountability Act (HIPAA), at Emory Rollins School.
Exposure Assessment. We assessed two high-resolution, speciated PM 2.5 data sets from two independent sources for the contiguous United States from 2000 to 2017. The first set of yearly mean predictions for PM 2.5 mass and six key components (Exposure I) was assessed at a resolution of 1 km 2 by van Donkelaar et al. (2019). 15 The monthly mean PM 2.5 total mass concentrations were calculated using satellite retrievals of aerosol optical depth and a chemical transport model (CTM) and then statistically integrated with 3364 ground-based PM 2.5 observations using geographically weighted regression. The six PM 2.5 components were then estimated by breaking down the total mass of PM 2.5 into specific chemical components based on CTM output and further calibrated using data from 829 unique ground-based compositional monitoring sites (402 to 821 sites depending on the chemical component). Compared with ground measurements, the predictions showed good long-term spatial agreement with cross-validated The second set of yearly mean concentrations for key PM 2.5 components (Exposure II) was predicted using super-learning and ensemble weighted-averaging of machine learning (ML) models, with spatial resolutions of 50 m in urban areas and 1 km 2 in non-urban areas. 16 Specifically, PM 2.5 component measurements were gathered from 987 monitoring sites, and the model also took into account hundreds of additional predictors, such as traffic counts, satellite observations, CTM simulations, and meteorological variables. Six ML models in non-urban areas and three MLs in urban areas were used to forecast each component, then ensemble weighted-averaging models and multiple super-learners were applied to combine the estimates. This approach produced outstanding model performance, with a test set cross-validated R 2 ranging from 0.86 to 0.96. Besides the six PM 2.5 components, we previously estimated PM 2.5 mass concentrations over the contiguous U.S. using an ensemble model that included hundreds of predictors and several machine learners, with a cross-validated R 2 of 0.89 for annual predictions. 17 Many other epidemiological studies have made extensive use of this PM 2.5 mass data set. 2,18 For each year in the study period, we calculated two sets of yearly mean concentrations for PM 2.5 total mass and each chemical component for each ZIP code and assigned exposure values depending on the residential ZIP code of each study participant. Each subject's residential ZIP code was updated annually, allowing us to track annual residential mobility.
Covariates. In addition to the individual-level demographic and Medicaid eligibility data, we also included various geographic and area-level covariates in our analysis. Data included county-level behavioral risk factors (mean body mass index and smoking prevalence), county-level health care capacity variables (number of active medical doctors and hospitals), ZIP-code level Socioeconomic Status (SES) variables (percent of population with less than high school education, median household income, percent of Black population, percent of population living below the poverty line, population density, and percent of population living in rented houses or apartments), gridded meteorological variables (yearly average relative humidity and temperature), and an indicator variable for geographical region from the Behavioral Risk Factor Surveillance System (BRFSS), American Community Survey (ACS), U.S. Census Bureau, and the North American Land Data Assimilation System (NLDAS) databases, respectively. Unless otherwise stated, all covariates were incorporated into the model as linear terms. Further details on all covariates have been previously described. 2 Statistical Analysis. Using single-component Cox proportional hazard models, which only included one PM 2.5 constituent at a time, we estimated the associations between each of the six chemical constituents of PM 2.5 and all-cause mortality in older adults between 2000 and 2017. We also stratified all models by age at entry, race, sex, Medicaid eligibility (a low SES indicator), and further adjusted for arealevel covariates mentioned above (e.g., behavioral risk factors, healthcare capacity, SES, and meteorological variables). To account for potential residual temporal and spatial variations, indicators of calendar year and geographic region were also incorporated into the model. All models accounted for residual autocorrelation within ZIP codes using a generalized estimating equation (GEE) 19 to obtain robust standard errors and 95% confidence intervals (CIs). All findings are shown as hazard ratios (HRs) with 95% CIs per interquartile range (IQR) increase and per 1 μg/m 3 increase in the mean yearly concentration of each PM 2.5 constituent.
For each component of PM 2.5 , we fitted penalized spline models while accounting for the same covariates across models to examine any nonlinearity between the components and allcause mortality. We introduced a penalized spline term for Environmental Science & Technology pubs.acs.org/est Article each relevant chemical compound in order to characterize the concentration−response (C−R) connections between each compound and mortality.
To assess the reliability of our key conclusions, we performed several sensitivity analyses. First, we fitted two sets of multi-component models by considering the potential collinearities between BC and OM and between SO 4 2− and NH 4 + ( Figure S1). The two multi-component models were specified by (1)  , and NO 3 − simultaneously (model 3). Second, we used an alternative exposure window with a 1 year lag period to evaluate the potential lagged effect of each compound on mortality. Mortality events were connected to exposures in the previous calendar year. Additionally, to account for potential measurement error caused by residential mobility, we performed a "non-movers" cohort analysis by restricting the analysis to individuals who remained in the same ZIP code throughout the follow-up period.
To investigate the effect modification of gender on the connections between PM 2.5 mass and the six chemical compounds' exposure and mortality, we stratified the data into two subgroups (male versus female), with separate regression models fit for each stratum.
R software, version 4.0.2, was employed for statistical analyses, and calculations for the analyses were performed on the Rollins HPC Cluster at Emory University. ■ RESULTS Table 1 presents descriptive statistics for our study population from 2000 through 2017. The cohort includes approximately 73.4 million participants, of which 44.0% were male, 99.4% were between the ages of 65 and 74 at the time of enrollment, 84.1% were white, and 9.8% were eligible for Medicaid. There were 29 million deaths among the cohort (39.6%), with approximately 669.6-million person-years of follow-up and a median follow-up of 8 years. Table S1 provides additional demographic information.
Using Exposure I data, for the period 2000−2017, the mean PM 2.5 mass concentration was 9. The spatial distributions of each chemical component were generally consistent between the two data sets (Figure 1). Figure S1 shows the correlation matrix among PM 2.5 mass and the six major compounds at the cohort level. PM 2.5 mass was highly correlated with BC, OM, NO 3 − , SO 4 2− , and NH 4 + in Exposure I (r values range from 0.66 to 0.81) and highly correlated with NH 4 + (r = 0.81) and SO 4 2− (r = 0.74) in Exposure II. Strong correlations were also indicated by BC and OM (r = 0.79 and 0.69) and SO 4 2− and NH 4 + (r = 0.80 and 0.85) in both exposure sets. Figure S2 shows the average chemical composition of the six components of PM 2.5 total mass in each exposure data set. Between 2000 and 2017, we observed similar proportions between chemical components across exposure data sets, with OM and SO 4 2− accounting for the largest proportions of total PM 2.5 mass concentrations.
Using Exposure I data, the single-component models indicate a significant positive relationship between long-term exposure to PM 2.5 total mass and its six major compounds and all-cause mortality (Figure 2   Our findings were robust to several sensitivity analyses. First, under multi-component models, we found similar results for most PM 2.5 components, excluding DUST, which yielded a weaker, non-significant association after adjusting for other PM 2.5 components using Exposure I data (Table S3). Second, we observed consistency in our results after specifying a 1 year lag between annual exposure for each chemical component and mortality (Table S4). Lastly, we found minimal bias due to residential mobility in our analysis of the "non-movers" cohort (Table S5). Table S6 shows subgroup-specific results stratified by gender. In both gender strata, we discovered that the total mass of PM 2.5 and its six main compounds remained significantly positively associated with all-cause death. Using the estimations from Exposures I and II, comparable patterns were discovered in most cases. Effect estimates for OM and SO 4 2− were higher among male subjects in both exposure data. For NO 3 − and NH 4 + , effect estimates were higher among female subjects in both exposure data.

■ DISCUSSION
We used two independently sourced data sets of highresolution speciated PM 2.5 data to estimate the long-term effects of exposure to PM 2.5 chemical components on all-cause mortality in a nationwide, population-based cohort of older adults. We found that long-term exposure to PM 2.5 total mass and its key chemical constituents was significantly associated with an increased risk of all-cause mortality among U.S. older adults. Specifically, among each of the six key compounds of PM 2.5 studied, we found the strongest associations for SO 4 2− , NH 4 + , and BC, while DUST had a relatively weaker impact, which is consistent with literature that DUST is typically not the predominant factor. 20 Adjusting for other pollutants in multi-component models only modestly changed the effect estimates except for DUST, indicating that the other components did not confound the observed associations.
Overall, SO 4 21 Previous large-scale epidemiology studies also suggested that secondary inorganic aerosols were connected to all-cause, cardiovascular disease, and cardiopul-   22 China, 23 and U.S. 24 Sulfur dioxide (SO 4 2− precursor) has natural sources (e.g., oceans and volcanic emissions) and anthropogenic emissions primarily from fossil fuel combustion. SO 4 2− can alter bronchial mucociliary transport in humans, 25 change the alveolar macrophage function, 26 and influence aortic contraction. 7 Additionally, SO 4 2− provides an acidic environment in the atmosphere and facilitates the solubility and bioavailability of trace metals in fine particulate matter, 27 which in turn causes reactive oxygen species (ROS) to be produced. ROS cause oxidative stress, inflammation, and genotoxicity, which are conditions that damage cellular physiological processes. 28 Nitrogen dioxide, an NO 3 − precursor, is primarily derived from fossil fuel combustion. Previous studies suggest that exposure to NO 3 − is associated with a circulatory biomarker of tumor necrosis factor alpha (TNF-α). 29 An elevated level of TNF-α may play a role in vascular dysfunction of the cardiovascular system, atherosclerosis formation and progression, and negative cardiac remodeling after myocardial infarction and heart failure. 30 In addition, animal studies have reported that exposure to NO 3 − may result in lung inflammatory cell infiltration, alveoli collapse, and thickening of the small airway wall. 31 We also discovered a strong link between NH 4 + and mortality, and the strength of the observed association was consistent with previous studies 23,32 that the effect of NH 4 + ranked top among the contributing factors.
However, it is unclear whether the observed associations are due to their intrinsic toxicity or because they are associated with other combustion-emitted culprit pollutants. BC was observed to have the largest effect estimates on mortality per 1 μg/m 3 change in exposure, although a 1 μg/m 3 increase is a much larger relative increase for BC than other components studied (e.g., OM and sulfate) as shown in Table  1. BC emissions mainly come from incomplete combustion of biomass and fossil fuel and traffic emission. 33 We observed larger effect sizes using Exposure II data compared to Exposure I (Figure 2). This discrepancy in the effect size for BC may be explained, in part, by the discrepancy in BC concentrations of the two exposure sets that relied on monitored data from different sources (i.e., thermal method vs optical method). There is some epidemiological evidence linking BC with mortality. BC has been considered as one of the specific markers for traffic-related air pollution, 34 and traffic-related air pollution has already been linked with lung cancer 35 and cardiorespiratory deaths 36 in many previous epidemiology studies. A World Health Organization (WHO) report summarized associations between BC with all-cause, cardiopulmonary, and cardiovascular mortality. 37 A previous large cohort study from the American Cancer Society also reported that coal combustion and fossil fuel combustion PM 2.5 were strongly and robustly associated with ischemic heart disease mortality. 38 The underlying biological mechanism of association between BC exposure and mortality can be characterized by the cardiopulmonary system. It is generally understood that exposure may induce oxidative DNA damage, increase levels of inflammatory mediators and oxidative stress, in addition to blood−brain barrier disruption, which can facilitate cardiovascular diseases such as hypertension, atherosclerosis, and stroke. 8,39−41 One in vitro study 42 suggests that BC could directly affect vascular endothelium triggering cytotoxic injury, inflammatory responses, and cell growth suppression.
Another chemical component linked to premature mortality is OM, which constituted a large fraction of PM 2.5 mass in both exposure data sets in the present study ( Figure S2). OM can be released directly from biogenic sources and combustion emissions, or secondarily formed through oxidation of volatile organic compounds (VOCs) and reactions that transform VOCs into low-vapor-pressure compounds that can condense on existing particles. 43 The latter secondary formation process for OM can change the toxicity of original particles. For instance, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) are highly toxic OM species, which are always found in OC. 44 PAHs and PCBs are known to cause a variety of adverse health effects in the reproductive system, immune system, and nervous system. 45,46 Additionally, OM can induce adverse cardiovascular effects via negative changes in blood pressure, heart rate variability, and worsening biomarker levels reflecting inflammation, hemostasis, and oxidative stress. 29,47,48 We observed linear relationships with BC (Exposure II), SO 4 2− , and NH 4 + , with no indication of a threshold for either outcome, and the previously published studies in Southeastern U.S. and China also reported similar linear association for BC (Exposure II), NO 3 − , and SO 4 2− . 20, 23 We observed nonlinear "bell-shaped" C−R relationships between exposure to BC (Exposure I), OM, and DUST and mortality, indicating that the relationships are increasing steeply at low to moderate exposure levels and leveled off at high exposure levels. There have been many theories proposed to explain the reasons ). The dotted lines represent PM 2.5 mass results. The error bars represent the 95% confidence intervals, while the calculated HRs were determined from single-component models. Air pollutants derived from two exposure models are distinguished using light and dark colors, with the light color denoting Exposure I data and the dark color denoting Exposure II data. Table S3 provides the corresponding hazard ratio values (model 1).

Environmental Science & Technology
pubs.acs.org/est Article causing a nonlinear C−R relationship, including preferential avoidance based on symptoms, decreased inhalation, and errors in estimating pollution exposure levels at elevated concentrations. 49 Furthermore, different C−R curves might result from the variations in population distributions across different components. Various study areas, populations, exposure time windows, and other factors could account for the disparity in C−R associations between our study and other studies. 50 According to our knowledge, this is the first nationwide cohort research to explore relationships between major chemical components of PM 2.5 and all-cause mortality in the U.S. In Europe, there are studies investigating the relationship between long-term exposure to PM 2.5 metal constituents and mortality, but the results are inconsistent. Chen et al. 32 reported in single pollutant models that all eight metals (Fe, Cu, K, S, Ni, Si, Zn, and V) had statistically significant associations with natural-cause mortality with HRs ranging from 1.05 to 1.27. But Wang et al. 51 did not report any significant relationships between cardiovascular deaths and those eight metals. Our component-specific study provides novel insight into the long-term effects of exposure to PM 2.5 focusing on the individual impact of the main chemical compounds of PM 2.5 total mass on mortality. Moreover, the use of two independently sourced, high-resolution exposure data sets allowed us to assess the validity of our results under different exposure assessment models, increasing our confidence in observed associations. We treated the two exposure data sets equally in this study, since the models from which the two exposures were derived were based on different algorithms, and each of the methods has its respective pros and cons. Even though we used both exposure data sets to explore the relationship between PM 2.5 constituents and allcause mortality, consistent results of single-and multiconstituent models were still observed, and this can strongly demonstrate the robustness of this association. Additionally, the large national cohort provides sufficient statistical power to capture complicated spatiotemporal patterns and variations in PM 2.5 composition and mortality risk, which may otherwise bias results in small samples.
Our study has several limitations. First, although the exposure assessment model achieved a high performance, the use of projected surface pollutant concentrations may still result in measurement error, despite showing strong model performance. Second, although our statistical models were adjusted for many potential confounders, we acknowledge that unmeasured individual-level risk factors (e.g., smoking, drug use, alcohol use) linked to premature mortality may have biased risk estimates. 52,53 However, the individual level variables were less likely to be confounders since our exposure was assigned on a ZIP code level. Furthermore, multicollinearity is likely due to correlations among the chemical components of PM 2.5. as such computationally scalable speciation techniques are needed to address this issue in large-scale epidemiological studies. Although the six major chemical components explored in this study accounted for Environmental Science & Technology pubs.acs.org/est Article most of PM 2.5 total mass, we cannot rule out the possibility that other unexamined and potentially correlated components confer risk. Lastly, studies of PM 2.5 components may be hard to interpret because the identification of individual emission sources of PM 2.5 chemical components, particularly the extent of spatial variability in local sources, is a considerable challenge. Future studies assessing source-specific effects of PM 2.5 will be important as they can be readily translatable into more targeted and effective air pollution control policies.
In conclusion, our study demonstrates that long-term exposure to PM 2.5 mass and its major components was related to an elevated risk of all-cause mortality among U.S. older adults. Reductions of PM 2.5 emission sources, such as fossil fuel burning (especially for BC exposure in high level areas), and traffic and power plants emissions, can lead to substantial public health benefits. ■ ASSOCIATED CONTENT
Details regarding area-level covariates; sensitivity analysis; correlations among PM 2.5 and its six major components (BC, OM, soil dust, nitrate, sulfate, and ammonium); and averaged chemical composition of PM 2.5 for two air pollution data sets (PDF) ■ AUTHOR INFORMATION